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Image Data Hiding Scheme using Adversarial Embedding

Image Data Hiding Scheme using Adversarial Embedding

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Varman, Raahul Malathi, P. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Data and information are increasingly at risk due to developments in science and technology. Several strategies for hiding information have been proposed to deal with these threats. Hiding information in a picture is the most commonly used method since it is difficult for an outsider to detect hidden information. Data coverage can be improved by using different deep learning and artificial intelligence techniques. This paper proposes a deep learning based framework steganography embedding-extraction architecture for embedding pictures as payloads. To achieve this, we have made some important contributions: (i) the general encoder-coder architecture, which encodes and decodes all sequences of pixels, is proposed;(ii) we are introducing a loss function that ensures the training of embedded extraction networks;(iii) on a range of freely available datasets, we will be doing a thorough analysis of the proposal for architecture;and (iv) to enhance security and quality of image, we are introducing a new adversary network for the detection of stego images. Indicate the high levels of PSNR and SSIM with cutting-edge payload capacities. © 2023 IEEE.

关键词： Adversarial networks Convolutional networks Deep learning Steganography

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Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network 7th

Spatio-Temporal Prediction in Epidemiology Using Graph Conv...

引用

7th International Conference on Information and Communication Technology for Intelligent systems, ICTIS 2023

作者： Siji Rani, S. Dhanyalaxmi, Panickar Akshay, A.S. Ananthakrishnan, K.M. Siva Sankar, A. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819937608

Over the last decade, e-commerce has expanded significantly. An effective recommendation system is needed for better information filtering as more products are available online, particularly clothing and fashion accessories. The literature already includes a number of different recommendations for clothing. However, new problems like computation complexity and data’s exponential growth appear. The recommendation model must be updated frequently because trends change quickly. This paper proposes a collaborative filtering-based recommendation system. The Nearest Neighbor PageRank (NNPR) ranking algorithm, which creates individualized recommendations, combines the PageRank algorithm and the user’s closest neighbors. Unlike the Alternating Least square method, the suggested model is assessed by utilizing the (ALS) algorithm. The experiments use a dataset of Amazon fashion reviews, and the results are recorded in terms of Hit Ratio (HR) and Average Reciprocal Rank (MRR). NNPR has been shown to outperform ALS in active users and cold starters scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Ensemble Machine Learning Models in Predicting Personality Traits and Insights using Myers-Briggs Dataset 2

Ensemble Machine Learning Models in Predicting Personality T...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Kumar R, Prasanna Mohan G, Bharathi Sai, Gudivada Dhyana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Personality prediction refers to the use of machine learning techniques to predict an individual's personality traits based on various sources of data, such as text, images, and social media usage Personality traits refer to persistent patterns of behaviors, thoughts, and feelings that differentiate one individual from another. The prediction of people's personality traits based on their social media posts using various machine learning models. With the help of this model, a person's personality can be classified based on the 16 categories of Myers-Briggs personality types. With the availability of a huge amount of data on human behavior and personality traits, it is possible to train a machine-learning model and predict the personality trait of a person. The ML model assesses the person based on their social media posts. The data consists of the posts from social media and the personality type to which a person belongs. The model will be using the NLTK library to assess and pre-process the data. Here a model has been based on built four machine learning models, which include logistic regression, support vector machines (SVM), nave Bayes, and random forest. Finally, we compare the machine learning model results to determine which one is best based on evaluation metrics (accuracy score, geometric mean score, ROC-AUC score). Furthermore, this can be used in the personalization of online advertising ads and campaigns. Also, it can be used by social media companies to attract users based on their personality traits and preferences. © 2023 IEEE.

关键词： Logistic regression

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Automated Ship Classification and Tracking in Satellite Imagery using Advanced Deep Learning Models 14

Automated Ship Classification and Tracking in Satellite Imag...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sruthi, K. Vijai, Anupa Sujee, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350335095

The identification of ships using satellite imagery has been a subject of continuous research due to its vital importance in maritime surveillance and navigation. It is challenging to recognize ships in satellite photographs due to the wide range of ship sizes and the complex backgrounds. In this study, we propose a system for classifying ships that combines the benefits of two deep-learning models, YOLOv3 and DenseNet. We began using YOLOv3 because it is a well-liked object identification model. However, we discovered that YOLOv3 is unable to recognize small objects, such as miniature ships, in satellite photographs. To get around this problem, we employed DenseNet, which is known for its ability to distinguish both large and small objects. However, DenseNet requires a significant amount of RAM for computations, which might be a disadvantage when resources are few. To get around this limitation, we coupled ResNet with DenseNet, a popular deep learning model recognized for its processing efficiency. As demonstrated by our recommended solution, which effectively recognizes ships in satellite data by combining the advantages of YOLOv3, DenseNet, and ResNet, ResNet and DenseNet working together achieved greater accuracy and more efficient use of memory. Our results demonstrate the efficacy of our approach and the potential applications to real-world issues like maritime surveillance and navigation. © 2023 IEEE.

关键词： Ships

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Cultivating Customer Purchase Intent: Leveraging Machine Learning for Precise Predictions 12

Cultivating Customer Purchase Intent: Leveraging Machine Lea...

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12th International Conference on system Modeling and Advancement in Research Trends, SMART 2023

作者： Krishna, R. Dhanush Mahadev, M. Hariprasad, S. Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369861

Insurance firms must employ cost-effective client engagement tactics in order to maximize the effectiveness of their marketing campaigns in the ever-changing business landscape of today. This work uses machine learning to solve the problem of effectively identifying future insurance subscribers. To build a prediction model, we utilize past marketing data from a modern insurance provider. The model makes use of historical campaign data, such as call duration, frequency of contact, and previous campaign outcomes, as well as client variables including age, job type, marital status, educational background, and contact communication style. Through the analysis of these characteristics, our machine learning model predicts the probability of a customer subscribing to the insurance product, allowing the business to focus on high-potential customers and reduce the expense of *** addition to offering insightful information about consumer behavior, the research helps to improve the general effectiveness of marketing tactics used by the insurance sector. © 2023 IEEE.

关键词： Insurance

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An Improved Machine Learning Approach to Detect Real Time Face Mask 5

An Improved Machine Learning Approach to Detect Real Time Fa...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Vipul, V. Manoj, Nanma Nair, Lekshmi S. Rani, S. Siji Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781665474672

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly. © 2023 IEEE.

关键词： Face recognition

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Development of Rakshak - A Risk Free App for People's Safety Using Map Embedded API

Development of Rakshak - A Risk Free App for People's Safety...

引用

2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Venkata Murari, L.M.A. Shashank, Marneni Mani Kumar, Sripathi Ravi Kumar, Parimi Saketh Ashwini, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350301595

Safety in India is broadly talked about everywhere these days. It has now turned into a significant issue. The crime percentage is on the spike. People are neither protected outside nor at home. Also, explorers from different nations are likewise in a questionable state while pondering coming to India. There are regulations, however there must be appropriate safety estimates which we need to be followed stringently to shield them from violence. A personal safety or SOS mobile application can be thought as the much-needed solution that can be used to aid personal safety. Such apps received increased prominence in the media after the 2012 Delhi gang rape case and consequent protests against 'brutal rapes, molestation and mistreatment of women'. Technology, including social and mobile tools, are playing an important role in improving overall personal safety, with technologies such as GPS, SMS, video, alerts, alarms, and more, allowing a user in distress to alert bystanders and close friends. In this paper, an android application to track people in need of help using the Global Positioning system is proposed and developed. The application can share their location with the nearest help-center and also to the nearby people along with voice recordings to analyze what is actually happening at the victim's place. The developed app will update the registered contacts on the user's location every few seconds. The GPS locations and recorded contacts are frequently stored in a database. The victim's location is communicated with greatest accuracy. © 2023 IEEE.

关键词： Global positioning system

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AI-Enabled Fast Detection of DDoS and Adversary DDoS Attacks in SDN 14

AI-Enabled Fast Detection of DDoS and Adversary DDoS Attacks...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Prathap, Pratheeksha Duttagupta, Subhasri Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350335095

The importance of network services especially telemedicine services has increased dramatically after the global spread of the Covid-19 virus. Telemedicine services may include live audio and video chats, uploading of images or files etc. in real-time. The efficiency of a network service relies on the dependability and resilience of its foundational infrastructure. The rapid increase of telemedicine services has also resulted in a steady rise in security attack methods, the breadth of their effects, and the ferocity with which they are launched. Distributed denial-of-service (DDoS) attacks have become a major source of worry for Internet service providers. Software Defined Network (SDN), in which the control plane is shifted to a logically centralized location, is widely used for providing large-scale Internet services due to advantages such as programmability, flexibility, etc. But a logically centralized control plane in SDN makes it more vulnerable to DDoS attacks due to a larger attack surface. In this paper, we focus on DDoS attack detection approaches and their adversarial behavior in SDN using the latest Deep Learning techniques. Our technique is able to achieve a detection accuracy of 98.49% for the primary DDoS dataset and 93.19% for the adversary dataset generated by us. © 2023 IEEE.

关键词： Denial-of-service attack

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AutoBots: A Botnet Intrusion Detection Scheme Using Deep Autoencoders 4th

AutoBots: A Botnet Intrusion Detection Scheme Using Deep Aut...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Verma, Ashwin Bhattacharya, Pronaya Prasad, Vivek Kumar Datt, Rajan Tanwar, Sudeep Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata700135 India

ISBN: (纸本)9789819914784

Recently, with the massive exchange of data over Internet of Things (IoT) ecosystems, attacks surfaces have also intensified. In IoT, connected devices share data over open channels and thus are highly vulnerable to security and privacy attacks. Botnet-based attacks have been found to have a significant effect on the network-based system. Thus, in this paper, we present a scheme AutoBots, which differentiates the normal and anomaly behaviour of IoT devices among the connected network. To exploit this, we consider diverse parameters like network behaviour profiles and apply autoencoders to classify and detect anomalous traffic from normal traffic. We used the BASHLITE and MIRAI IoT botnet setup and trained our network with the N-BaIoT dataset that has both benign and malicious network traffic. We compared our scheme for metrics like attack detection time, attack detection with respect to hourly traffic, deep residual accuracy, and residual loss. The presented results signify the efficacy of the proposed scheme against conventional bot-detection schemes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Hierarchical ST-CEN with Dynamic Attention Mechanisms for Enhanced Deepfake Image Detection 3

Hierarchical ST-CEN with Dynamic Attention Mechanisms for En...

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3rd International Conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Prabith, G.S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (数字)9798350343922

ISBN: (纸本)9798350343922

The increasing complexity of deepfake technologies necessitates the development of detection methods to counter their harmful propagation. This research introduces a ground-breaking approach, to detecting deepfake images by utilizing Hierarchical Spatial Temporal Contextual Embedding Networks (HST CEN) in combination with Dynamic Attention Mechanisms (DAM).By addressing the challenges in understanding temporal context within deepfake images HST CEN incorporates multi level contextual embeddings that capture subtle spatial and temporal cues. At the time DAM employs evolving attention mechanisms that hierarchically identify important features at multiple levels enabling robust detection of manipulated *** methodology includes an evaluation on the ForgeryNet dataset showcasing the superiority of the model in discerning deepfake images compared to architectures. The results demonstrate performance leading to improved accuracy, specificity and generalization across various forgery techniques and different identity contexts. The qualitative analysis reveals attention value distributions providing insights into how the model identifies manipulated features for nuanced analysis, by end users. The impacts of this detection method have implications, in scenarios providing a strong defense against the spread of sophisticated deepfake content. The suggested HST CEN with DAM not enhances the capabilities of identifying manipulated images but also highlights the importance of having a deep understanding of spatial temporal context and utilizing dynamic attention mechanisms to combat the widespread use of artificially created visuals, in todays digital world. © 2023 IEEE.

关键词： Image enhancement

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